research:protosel

Prototype selection

A list of paper related to instance selection and construction, metric learning and sparse kernel methods.

This list also includes papers written by us

Total numer of publications: 95

submitted (2)

K. Grudzinski E$k$P: A Fast Minimization–Based Prototype Selection Algorithm. Control and Cybernetics submitted

K. Grudzinski Selection of Prototypes with the E$k$P System. Control and Cybernetics submitted

2012 (5)

M. Blachnik, M. Kordos Extraction of prototype-based threshold rules using neural training procedure. LNCS 7553 pp. 255–262. 2012

T. Maszczyk, W. Duch, M. Blachnik Feature ranking methods used for selection of prototypes. LNCS 7553 2012

M. Kordos, M. Blachnik Instance Selection with Neural Networks for Regression Problems. LNCS 7553 pp. 263–270. 2012

M. Blachnik, M. Kordos Computational Complexity Reduction and Interpretability Improvement of Distance-based Decision Trees.. LNCS 7208 pp. 288-297. 2012

M. Blachnik, M. Kordos, T. Wieczorek, S. Golak Selecting Representative Prototypes for Prediction the Oxygen Activity in Electric Arc Furnace. LNCS 2012

2011 (3)

M. Blachnik, W. Duch LVQ algorithm with instance weighting for generation of prototype-based rules.. Neural Networks Elsevir. 2011

M. Blachnik, M. Kordos Simplnifying SVM with Weighted LVQ Algorithm. LNCS 6936 pp. 212-219. 2011

M. Blachnik, M. Kordos Instance Selection and Prototype Based Rules. A new extension to RapidMiner. In Proceedings of RCoMM. 2011

2010 (3)

M. Kordos, D. Strzempa, M. Blachnik Do We Need Whatever More than k-NN?. LNCS 6113 pp. 414-421. 2010

M. Blachnik, W. Duch Improving Accuracy of LVQ Algorithm by Instance Weighting. LNCS 6354 pp. 257-266. 2010

Garcia Salvador, Derrac Joaquin, Cano Jose Ramon, Herrera Francisco Prototype Selection for Nearest Neighbor Classification: Taxonomy and Empirical Study. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 34 (3) pp. 417-435. 2010

2009 (3)

R. Min, D.A. Stanley, Z. Yuan, A. Bonner, Z. Zhang A deep non-linear feature mapping for large-margin kNN classification. In Data Mining, 2009. ICDM'09. Ninth IEEE International Conference on. pp. 357–366. 2009

M. Blachnik Comparison of Various Feature Selection Methods in Application to Prototype Best Rules. Advances in Intelligent and Soft Computing 57 pp. 257-264. Springer Verlag. 2009

K.Q. Weinberger, L.K. Saul Distance metric learning for large margin nearest neighbor classification. The Journal of Machine Learning Research 10 pp. 207–244. JMLR. org. 2009

2008 (5)

M. Blachnik, J. Laksonen Image Classification by Histogram Features Created With Learning Vector Quantization. LNCS 5163 2008

M. Blachnik, W. Duch Rule Extraction from Support Vector Machines. Springer. 2008

M. Blachnik, W. Duch Building Localized Basis Function Networks Using Context Dependent Clustering. LNCS 5163 2008

K.Q. Weinberger, L.K. Saul Fast solvers and efficient implementations for distance metric learning. In Proceedings of the 25th international conference on Machine learning. pp. 1160–1167. 2008

K. Schmidt, T. Behrens, T. Scholten Instance selection and classification tree analysis for large spatial datasets in digital soil mapping. Geoderma 146 (1) pp. 138–146. Elsevier. 2008

2007 (2)

Gan Guojun, Ma Chaoqun, Wu Jianhong Data Clustering: Theory, Algorithms, and Applications. ASA-SIAM. 2007

J. Antonia Jones, D. Evans, S.E. Kemp A note on the Gamma test analysis of noisy input output data and noisy time series. Physica D: Nonlinear Phenomena 229 (1) pp. 1-8. 2007

2006 (8)

E. P{ k{e}}kalska, R.P.W. Duin, P. Pacl{ ' i}k Prototype selection for dissimilarity-based classifiers. Pattern Recognition 39 (2) pp. 189–208. Elsevier. 2006

H. Zhang, A.C. Berg, M. Maire, J. Malik SVM-KNN: Discriminative nearest neighbor classification for visual category recognition. In Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on. pp. 2126–2136. 2006

M. Blachnik, W. Duch Prototype-based threshold rules. LNCS 4234 Physica Verlag, Springer. 2006

M. Blachnik, W. Duch, T. Wieczorek . LNCS 4029 pp. 573–582. Physica Verlag, Springer. 2006

T. Wieczorek, M. Blachnik, W. Duch Heterogeneous distance functions for prototype rules: influence of parameters on probability estimation. International Journal of Artificial Intelligence Studies 1 pp. xxx–yyy. 2006

K. Grudzinski Challenging Problems of Science: Computer Science. pp. 237–244. EXIT, Warsaw. 2006

M. Wu, B. Sch{ö}lkopf, G. Bakur A Direct Method for Building Sparse Kernel Learning. The JMLR 4 pp. 603–624. 2006

K.Q. Weinberger, J. Blitzer, L.K. Saul Distance metric learning for large margin nearest neighbor classification. In In NIPS. 2006

2005 (7)

M. Bereta Hybrid immune algorithm for feature selection and classification of ECG signals. In Methods of Artificial Intelligence. AI-METH Series. Gliwice. 2005

T. Wieczorek, M. Blachnik, W. Duch Influence of probability estimation parameters on stability of accuracy in prototype rules using heterogeneous distance functions. Artificial Intelligence Studies 2 pp. 71-78. 2005

M. Blachnik, W. Duch, T. Wieczorek Probabilistic distance measures for prototype based rules. In Proc. of ICONIP. pp. 445-450. Taiwan. 2005

M. Blachnik, W. Duch, T. Wieczorek Threshold rules decision list. In Methods of artificial intelligence. pp. 23–24. AI-METH Series. Gliwice. 2005

R.E. Fan, P.H. Chen, C.J. Lin Working set selection using the second order information for training SVM. JMLR 6 pp. 1889-1918. 2005

T. Wieczorek, M. Blachnik, W. Duch Influence of probability estimation parameters on stability of accuracy in prototype rules using heterogeneous distance functions. In Proceedings of Artificial Intelligence Studies. pp. Vol.2. Siedlce. 2005

D. Evans Estimating the variance of multiplicative noise. In Noise and Fluctuations: 18th International Conference on Noise and Fluctuations - ICNF 2005. pp. 99-102. 2005

2004 (4)

K. Grudzinski SBL–PM-M: A System for Partial Memory Learning. Lecture Notes in Computer Science 3070 pp. 586–591. 2004

W. Duch, M. Blachnik Fuzzy rule-based systems derived from similarity to prototypes. In LNCS. pp. 912–917. Physica Verlag, Springer. New York. 2004

N. Jankowski, M. Grochowski Comparison of Instance Selection Algorithms. I. Algorithms Survey. Lecture Notes in Computer Science 3070 pp. 598–603. 2004

M. Grochowski, N. Jankowski Comparison of Instance Selection Algorithms. II. Results and Comments. LNCS 3070 pp. 580–585. 2004

2003 (4)

K. Lin, C. Lin A study on reduced support vector machines. IEEE Transactions on Neural Networks 14 (6) pp. 1449–1459. 2003

J.T. Kwok, I.W. Tsang The pre-image problem in kernel methods. IEEE Transactions on Neural Networks 15 pp. 408–415. 2003

J.R. Cano, F. Herrera, M. Lozano Using evolutionary algorithms as instance selection for data reduction in KDD: an experimental study. Evolutionary Computation, IEEE Transactions on 7 (6) pp. 561–575. IEEE. 2003

C. Zeng, C.X. Xing, L.Z. Zhou Similarity measure and instance selection for collaborative filtering. In Proceedings of the 12th international conference on World Wide Web. pp. 652–658. 2003

2002 (5)

T. Joachims Learning to Classify Text Using Support Vector Machines. Kluwer Academic Publisher. 2002

K. Gr{ c a}bczewski, W. Duch Heterogenous forests of decision trees. Springer Lecture Notes in Computer Science 2415 pp. 504–509. Physica Verlag, Springer. 2002

Learning with Kernels.. MIT Press, Cambridge. 2002

H. Brighton, C. Mellish Advances in instance selection for instance-based learning algorithms. Data mining and knowledge discovery 6 (2) pp. 153–172. Springer. 2002

H. Liu, H. Motoda On issues of instance selection. Data Mining and Knowledge Discovery 6 (2) pp. 115–130. Springer. 2002

2001 (8)

H. Liu, H. Motoda Instance selection and construction for data mining. Springer. 2001

W. Duch, K. Grudzi 'nski Prototype based rules - new way to understand the data. In IEEE International Joint Conference on Neural Networks. pp. 1858–1863. IEEE Press. Washington D.C. 2001

W. Duch, K. Grudzi 'nski Prototype based rules - new way to understand the data. In IEEE International Joint Conference on Neural Networks. pp. 1858–1863. IEEE Press. Washington D.C. 2001

Ordered weighted generalized conditional possibilistic clustering. In Zbiory rozmyte i ich zastosowania. pp. 469–

R. Collobert, S Bengio {SVMTorch}: Support Vector Machines for Large-Scale Regression Problems. JMLR 1 pp. 143–160. 2001

T. Downs, K. Gates, A. Masters Exact Simplification of Support Vector Solutions. The JMLR 2 pp. 293–297. 2001

T. Kohonen Self-Organizing Maps. Springer Verlag. 2001

F. Schwenker, H. Kestler, G. Palm Three learning phases for radial-basis-function networks. Neural Networks 14 pp. 439-458. 2001

2000 (7)

D.R. Wilson, T.R. Martinez Reduction techniques for instance-based learning algorithms. ML 38 pp. 257-268. 2000

M. Tipping The relevance vector machine. In Advances in Neural Information Processing Systems. Morgan Kaufmann. 2000

L.I. Kuncheva Fuzzy Classifier Design. Physica-Verlag. 2000

A new generalized weighted conditional fuzzy clustering. BUSEFAL 81 pp. 8–16. 2000

N. Jankowski Ontogeniczne Sieci Neuronowe. In Sieci Neuronowe. 2000

W. Duch, R. Adamczak, G.H.F. Diercksen Classification, Association and Pattern Completion using Neural Similarity Based Methods. Applied Mathematics and Computer Science 10 pp. 101–120. 2000

S. Bermejo Learning with nearest neighbor classifiers. 2000

1999 (6)

J. Platt Using Sparseness and Analytic QP to Speed Training of Support Vector Machines. Advances in Neural Information Processing Systems 11 MIT Press. 1999

L. Kuncheva, J.C. Bezdek Presupervised and postsupervised prototype classifier design. IEEE Transactions on Neural Networks 10 (5) pp. 1142-1152. 1999

W. Duch, R. Adamczak, G.H.F. Diercksen Distance-based multilayer perceptrons. In International Conference on Computational Intelligence for Modelling Control and Automation. pp. 75–80. IOS Press. Amsterdam, The Netherlands. 1999

N. Jankowski Flexible Transfer Functions with Ontogenic Neural. Computational Intelligence Lab, DCM NCU. Toru 'n, Poland. 1999

Xuhui Shao, V. Cherkassky Multi-resolution support vector machine. In International Joint Conference on Neural Networks. 1999

F. Hoppner, F. Klawonn, R. Kruse, T. Runkler Fuzzy Cluster Analysis. Wiley. 1999

1998 (6)

B. Sch{ö}lkopf, P. Knirsch, A. Smola, C. Burges Fast approximation of support vector kernel expansions. Informatik Aktuell, Mustererkennung 1998

W. Pedrycz Conditional Fuzzy Clustering in the Design of Radial Basis Function Neural Networks. IEEE Transactions on Neural Networks 9 (4) IEEE Press. 1998

L. Kuncheva, J.C. Bezdek Nearest prototype classification: Clustering, genetic algorithms or random search?. IEEE Transactions on Systems, Man, and Cybernetics C28 (1) pp. 160-164. 1998

L.I. Kuncheva, J.C. Bezdek An Integrated Framework for Generalized Nearest Prototype Classifier Design. International Journal of Uncertainty 6 (5) pp. 437-457. 1998

W. Pedrycz Fuzzy set technology in knowledge discover. Fuzzy Sets and Systems 98 (3) pp. 279-290. 1998

L.I. Kuncheva, J.C. Bezdek Nearest prototype classification: Clustering, genetic algorithms, or random search?. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on 28 (1) pp. 160–164. IEEE. 1998

1997 (4)

N. Jankowski, V. Kadirkamanathan Statistical Control of Growing and Pruning in {RBF}-like Neural Networks. In Third Conference on Neural Networks and Their Applications. pp. 663–670. Kule, Poland. oct 1997

N. Jankowski, V. Kadirkamanathan Statistical Control of {RBF}-like Networks for Classification. In 7th International Conference on Artificial Neural Networks. pp. 385–390. Springer-Verlag. Lausanne, Switzerland. oct 1997

D.B. Skalak Prototype selection for composite nearest neighbor classifiers. 1997

JS S{á}nchez, F. Pla, FJ Ferri Prototype selection for the nearest neighbour rule through proximity graphs. Pattern Recognition Letters 18 (6) pp. 507–513. Elsevier. 1997

1996 (3)

W. Pedrycz Conditional Fuzzy C-Means. Pattern Recognition Letters 17 pp. 625-632. 1996

C. Burges Simplified Support Vector Decision Rules. In ICML. pp. 71–77. 1996

R. Jang, C.T. Sun Functional Equivalence between Radial Basis Functions Networks and Fuzzy Inference Systems. IEEE Transactions on Neural Networks 1996

1995 (2)

R. Cameron-Jones Instance Selection by Encoding Length Heuristic with Random Mutation Hill Climbing. In Proc. of the Eighth Australian Joint Conference on Artificial Intelligence pp. 99-106. 1995

W. Duch, G.H.F. Diercksen Feature Space Mapping as a Universal Adaptive System. Computer Physics Communications 87 pp. 341–371. 1995

1994 (2)

D.B. Skalak Prototype and feature selection by sampling and random mutation hill climbing algorithms. In Proceedings of the eleventh international conference on machine learning. pp. 293–301. 1994

M. Schwabacher, H. Hirsh, T. Ellman Learning prototype-selection rules for case-based iterative design. In Artificial Intelligence for Applications, 1994., Proceedings of the Tenth Conference on. pp. 56–62. 1994

1991 (1)

D. Aha, D. Kibler, M.K. Albert Instance-Based Learning Algorithms. Machine Learning 6 pp. 37-66. Kluwer Academic Publishers. 1991

1986 (1)

C. Stanfill, D. Waltz Toward memory-based reasoning. Communications of the ACM 29 (12) pp. 1213-1228. 1986

1976 (1)

I. Tomek An experiment with the edited nearest-neighbor rule.. IEEE Trans. on Systems, Man, and Cybernetics 6 pp. 448-452. 1976

1974 (1)

Chang Chin-Liang Finding Prototypes for Nearest Neighbor Classifiers.. IEEE Transactions on Computers 23 (11) pp. 1179-1184. 1974

1972 (1)

D.L. Wilson Assymptotic properties of nearest neighbour rules using edited data.. IEEE Trans. on Systems, Man, and Cybernetics SMC-2 pp. 408-421. 1972

1968 (1)

P.E. Hart The condensed nearest neighbor rule.. IEEE Trans. on Information Theory 16 pp. 515-516. 1968

research/protosel.txt · Last modified: 2014/05/19 17:57 (external edit)